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The famous Uzawa (1961) balanced growth theorem has exercised a tyranny of sorts over macroeconomics for decades. It is the prime reason why researchers use Cobb–Douglas production functions and abstract from considering movements in factor shares. Others have had to recourse to complex explanations for long-run labor augmentation in technical progress. In this chapter, we discuss the issues arising from this problem and propose a way of achieving balanced growth with a short-run production function where the elasticity of factor substitution is less than one, and capital augmenting technology shocks can be permanent. We do so by allowing firms to choose the relative reliance on capital in the production technology and introducing a suitable modification of the production function. We also provide some model simulations in the context of a simple deterministic neoclassical growth model.

Estimation of (in)efficiency became a popular practice that witnessed applications in virtually any sector of the economy over the last few decades. Many different models were deployed for such endeavors, with Stochastic Frontier Analysis (SFA) models dominating the econometric literature. Among the most popular variants of SFA are Aigner, Lovell, and Schmidt (1977), which launched the literature, and Kumbhakar, Ghosh, and McGuckin (1991), which pioneered the branch taking account of the (in)efficiency term via the so-called environmental variables or determinants of inefficiency. Focusing on these two prominent approaches in SFA, the goal of this chapter is to try to understand the production inefficiency of public hospitals in Queensland. While doing so, a recognized yet often overlooked phenomenon emerges where possible dramatic differences (and consequently very different policy implications) can be derived from different models, even within one paradigm of SFA models. This emphasizes the importance of exploring many alternative models, and scrutinizing their assumptions, before drawing policy implications, especially when such implications may substantially affect people’s lives, as is the case in the hospital sector.

This chapter provides a survey of alternative methodologies for measuring and comparing productivity and efficiency of airlines, and reviews representative empirical studies. The survey shows the apparent shift from index procedures and traditional OLS estimation of production and cost functions to stochastic frontier methods and Data Envelopment Analysis (DEA) methods over the past three decades. Most of the airline productivity and efficiency studies over the last decade adopt some variant of DEA methods. Researchers in the 1980s and 1990s were mostly interested in the effects of deregulation and liberalization on airline productivity and efficiency as well as the effects of ownership and governance structure. Since the 2000s, however, studies tend to focus on how business models and management strategies affect the performance of airlines. Environmental efficiency now becomes an important area of airline productivity and efficiency studies, focusing on CO₂ emission as a negative or undesirable output. Despite the fact that quality of service is an important aspect of airline business, limited attempts have been made to incorporate quality of service in productivity and efficiency analysis.

The distinction between discussing human capital (HC) and its actual measurement is the presence of indices and equations to substantiate the belief of measuring intangibles. The chapter makes a concise mention of research precedents, deriving leads for the foundation of HC. The chapter aims to provide clarity on the concept of HC measurement and bring to light the tools that can confer tangibility to intangibles. It argues that the measurement of HC is an achievable idea; furthering that a systematic review into the inter-disciplinary studies can offer viable solutions to the challenge of measuring intangibles. The chapter while discussing the contention makes a vivid mention of Bhutan’s gross national happiness (GNH), Happiness Seismograph, Cobb–Douglas model and others to make an impression on the minds of the reader.

The processes of liberalisation, globalisation and integration have brought new dynamics into banking markets. In an increasingly competitive environment, banks have been forced to refocus their strategies and examine their performance, because their survival in the 21st century will depend on efficiency (Denizer & Tarimcilar, 2001). In recent years, therefore, bank efficiency has received wide attention, and researchers have developed an extensive array of sophisticated methods and tools to estimate efficiency.

“Economics is a Serious Subject.” Edwin Cannan.

Existing theoretical and empirical evidence is inconclusive concerning the comparative performance of labor-managed firms (LMFs) and conventional firms. By assembling and analyzing new data for a sample of 51 conventional firms and 26 producer cooperatives in the Italian construction industry during the period 1981–1989 we provide additional evidence. Except for organizational form, the cooperatives in our sample are fairly comparable to our conventional firms. Based on our production function estimates, and unlike some previous econometric studies, we find no significant productivity advantage of cooperatives over conventional firms. Our ordinary least squares (OLS) point estimates generally indicated that output would be lower in a cooperative than in an otherwise identical conventional firm. The only statistically significant measure of financial and decision-making participation is collective reserves. We conclude by offering some possible explanations for why our results may differ from some previous findings, especially those for Italian producer cooperatives. In particular we suggest that research methods that are new to the study of cooperatives are needed to help to resolve these questions.

In this paper, we expand Kleibergen and Zivot's (2003) Bayesian two-stage (B2S) model by allowing for unequal variances. Our choice for modeling heteroscedasticity is a fully Bayesian parametric approach. As an application, we present a cross-country Cobb–Douglas production function estimation.

The chapter examines the sources of total factor productivity growth (TFPG) of the 2-digit manufacturing industries as well as total manufacturing industry of Gujarat during the period from 1981–82 to 2010–11, using a stochastic frontier approach. The empirical finding clearly states that although factor accumulations as well as resource allocations in most of the 2-digit manufacturing industries of the state have been improved during the postreform period, technological progress (TP) and technical efficiency change (TEC) of the same have deteriorated in most industries of the state during that period. As a result TFPG in the major manufacturing industries as well as total manufacturing industry of the state have declined because the combined effect of their improvement in scale effect (SC) and allocation efficiency effect (AEC) could not offset the declining effect of both the TP and TEC of the same during that period. In this context, the government should take some policy initiatives to improve productive efficiency of the organized manufacturing industries in Gujarat. Once efficiency increases, it enhances competitiveness, thereby increasing productivity growth and its different sources of organized manufacturing industries of the state.